Surgical Activities Recognition Using Multi-scale Recurrent Networks

Recently, surgical activity recognition has been receiving significant attention from the medical imaging community. Existing state-of-the-art approaches employ recurrent neural networks such as long-short term memory networks (LSTMs). However, our experiments show that these networks are not effective in capturing the relationship of features with different temporal scales. Such limitation will lead to sub-optimal recognition performance of surgical activities containing complex motions at multiple time scales. To overcome this shortcoming, our paper proposes a multi-scale recurrent neural network (MS-RNN) that combines the strength of both wavelet scattering operations and LSTM. We validate the effectiveness of the proposed network using both real and synthetic datasets. Our experimental results show that MS-RNN outperforms state-of-the-art methods in surgical activity recognition by a significant margin. On a synthetic dataset, the proposed network achieves more than 90% classification accuracy while LSTM's accuracy is around chance level. Experiments on real surgical activity dataset shows a significant improvement of recognition accuracy over the current state of the art (90.2% versus 83.3%).